1. Lecture 5 Today, how to solve recurrences We learned “guess and proved by induction” We also learned “substitution” method Today, we learn the “master theorem” More divide and conquer: closest pair problem matrix multiplication

2. Master Theorem Theorem 4.1 (CLRS, Theorem 4.1) Let a ≥ 1 and b > 1 be constants. Let f(n) be a function and let T(n) be defined on the nonnegative integers by T(n) = aT(n/b) + f(n). Then

3. Note Only apply to a particular family of recurrences. f(n) is positive for large n. Key is to compare f(n) with n log_b a Case 2, more general is f(n) = Θ( n log_b a lg k n). Then the result is T(n) = Θ( n log_b a lg k+1 n). Sometimes it does not apply. Ex. T(n) = 4T(n/2) + n 2 /logn.

4. Proof ideas of Master Theorem Consider a tree with T(n) at the root, and apply the recursion to each node, until we get down to T(1) at the leaves. The first recursion is T(n) = aT(n/b) + f(n), so assign a cost of f(n) to the root. At the next level we have “a” nodes, each with a cost of T(n/b). When we apply the recursion again, we get a cost of af(n/b) for all of these. At the next level we have a 2 nodes, each with a cost of T(n/b 2 ). We get a cost of a 2 f(n/b 2 ). We continue down to T(1) at the leaves. There are a log_b n leaves and each costs Θ(1), which gives Θ(a log_b n ). The total cost associated with f is Σ 0 ≤ i ≤ log_b n - 1 a i f(n/b i ). Thus T(n) = Θ(n log_b a ) + Σ 0 ≤ i ≤ (log_b n) - 1 a i f(n/b i ). The three cases now come from deciding which term is dominant. In case (1), the Θ term is dominant. In case (2), the terms are roughly equal (but the second term has an extra lg n factor). In case (3), the f(n) term is dominant. The details are somewhat painful, but can be found in CLRS, pp. 76-84.

5. f (n/b) Idea of master theorem f (n/b)   (1) … Recursion tree: … f (n)f (n) a f (n/b 2 ) … a h = log b n f (n)f (n) a f (n/b) a 2 f (n/b 2 ) … #leaves = a h = a log b n = n log b a n log b a   (1)

6. Three common cases Compare f (n) with n log b a : 1. f (n) = O(n log b a –  ) for some constant  > 0. f (n) grows polynomially slower than n log b a (by an n  factor). Solution: T(n) =  (n log b a ).

7. f (n/b) Idea of master theorem f (n/b)   (1) … Recursion tree: … f (n)f (n) a f (n/b 2 ) … a h = log b n f (n)f (n) a f (n/b) a 2 f (n/b 2 ) … n log b a   (1) C ASE 1: The weight increases geometrically from the root to the leaves. The leaves hold a constant fraction of the total weight.  (n log b a ) These functions increase from top to bottom geometrically, hence we only need to have the last bottom term

8. Case 2 Compare f (n) with n log b a : 2. f (n) =  (n log b a lg k n) for some constant k  0. f (n) and n log b a grow at similar rates. This is clear for k=0. For k>0, the intuition is that lg k n factor remain for constant fraction of levels, hence sum to the following Solution: T(n) =  (n log b a lg k+1 n).

9. f (n/b) Idea of master theorem f (n/b)   (1) … Recursion tree: … f (n)f (n) a f (n/b 2 ) … a h = log b n f (n)f (n) a f (n/b) a 2 f (n/b 2 ) … n log b a   (1) C ASE 2: (k = 0) The weight is approximately the same on each of the log b n levels.  (n log b a lg n) All levels same

10. Case 3, c<1, a k f(n/b k ) geometrically decreases hence = Θ(f(n)) Compare f (n) with n log b a : 3. f (n) =  (n log b a +  ) for some constant  > 0. f (n) grows polynomially faster than n log b a (by an n  factor), and f (n) satisfies the regularity condition that a f (n/b)  c f (n) for some constant c < 1. Solution: T(n) =  ( f (n) ).

11. f (n/b) Idea of master theorem f (n/b)   (1) … Recursion tree: … f (n)f (n) a f (n/b 2 ) … a h = log b n f (n)f (n) a f (n/b) a 2 f (n/b 2 ) … n log b a   (1) C ASE 3: The weight decreases geometrically from the root to the leaves. The root holds a constant fraction of the total weight.  ( f (n)) af(n/b)<(1-ε)f(n)

12. Examples for the Master Theorem The Karatsuba recurrence has a = 3, b = 2, f(n) = cn. Then case 1 applies, and so T(n) = Θ(n l og_2 3 ), as we found. The mergesort recurrence has a = 2, b = 2, f(n) = n. Then case 2 applies, and so T(n) = Θ(n lg n). Finally, a recurrence like T(n) = 3T(n/2) + n 2 gives rise to case 3. In this case f(n) = n 2, so 3f(n/2) = 3 (n/2) 2 = (3/4) n 2 ≤ c n 2 for c = 3/4, and so T(n) = Θ(n 2 ). Note that the master theorem does not cover all cases. In particular, it does not cover the case T(n) = 2 T(n/2) + n / lg n since then the only applicable case is case 3, but then the inequality involving f does not hold.

13. Closest pair problem Input: A set of points P = {p 1,…, p n } in two dimensions Output: The pair of points p i, p j that minimize the Euclidean distance between them.

14. Distances Euclidean distance

15. Closest Pair Problem

17. Divide and Conquer O(n 2 ) time algorithm is easy Assumptions: No two points have the same x-coordinates No two points have the same y-coordinates How do we solve this problem in 1 dimension? Sort the number and walk from left to right to find minimum gap.

18. Divide and Conquer Divide and conquer has a chance to do better than O(n 2 ). We can first sort the points by their x- coordinates and sort also by y-coordinates

19. Closest Pair Problem

20. Divide and Conquer for the Closest Pair Problem Divide by x-median

21. Divide Divide by x-median L R

22. Conquer Conquer: Recursively solve L and R L R

23. Combination I Take the smaller one of  1,  2 :  = min(  1,  2 ) L R 22

24. Combination II Is there a point in L and a point in R whose distance is smaller than  ?  = min(  1,  2 ) L R

25. Combination II If the answer is “no” then we are done!!! If the answer is “yes” then the closest such pair forms the closest pair for the entire set How do we determine this?

26. Combination II Is there a point in L and a point in R whose distance is smaller than  ? L R

27. Need only to consider the narrow band O(n) time L R

28. Combination II Is there a point in L and a point in R whose distance is smaller than  ? Denote this set by S, assume S y is the sorted list of S by the y-coordinates. L R

29. Combination II There exists a point in L and a point in R whose distance is less than  if and only if there exist two points in S whose distance is less than . If S is the whole thing, did we gain anything? CLAIM: If s and t in S have the property that ||s- t|| < , then s and t are within 15 positions of each other in the sorted list S y.

30. Combination II Is there a point in L and a point in R whose distance is smaller than  ? L R There are at most one point in each box of size δ/2 by δ/2. Thus s and t cannot be too far apart.

31. Closest-Pair Preprocessing: Construct P x and P y as sorted-list by x- and y-coordinates Closest-pair(P, P x,P y ) Divide Construct L, L x, L y and R, R x, R y Conquer Let  1 = Closest-Pair(L, L x, L y ) Let  2 = Closest-Pair(R, R x, R y ) Combination Let  = min(  1,  2 ) Construct S and S y For each point in S y, check each of its next 15 points down the list If the distance is less than , update the  as this smaller distance

32. Complexity Analysis Preprocessing takes O(n lg n) time Divide takes O(n) time Conquer takes 2 T(n/2) time Combination takes O(n) time T(n) = 2T(n/2) + cn So totally takes O(n lg n) time.

33. Matrix Multiplication Suppose we multiply two NxN matrices together. Regular method is NxNxN = N 3 multiplications O(N 3 )

34. Can we Divide and Conquer? C11 = A11*B11 + A12*B21 C12 = A11*B12 + A12*B22 C21 = A21*B11 + A22*B21 C22 = A21*B12 + A22*B22 Complexity : T(N) = 8T(N/2) + O(N 2 ) = O(N log 2 8 ) = O(N 3 ) A = B = C= A*B = No improvement

35. Strassen’s Matrix Multiplication P 1 = (A 11 + A 22 )(B 11 +B 22 ) P 2 = (A 21 + A 22 ) * B 11 P 3 = A 11 * (B 12 - B 22 ) P 4 = A 22 * (B 21 - B 11 ) P 5 = (A 11 + A 12 ) * B 22 P 6 = (A 21 - A 11 ) * (B 11 + B 12 ) P 7 = (A 12 - A 22 ) * (B 21 + B 22 ) C 11 = P 1 + P 4 - P 5 + P 7 C 12 = P 3 + P 5 C 21 = P 2 + P 4 C 22 = P 1 + P 3 - P 2 + P 6 And do this recursively as usual. Volker Strassen

36. Time analysis T(n) = 7T(n/2) + O(n 2 ) = 7 logn by the Master Theorem =n log7 =n 2.81 Best bound: O(n 2.376 ) by Coppersmith- Winograd. Best known (trivial) lower bound: Ω(n 2 ). Open: what is the true complexity of matrix multiplication?